Behaviorla Analytics Filters

We next move to discuss how filters can be set-up by enterprises across multiple consumer channels – these filters represent conditions of events in the form of business rules – these conditional rules can be used to react and fire targeted offers of products, services or content. These conditional business rules take the form of signatures representing profitable customers and/or opportunities for enterprises for growth marketing and targeted offers to consumers.

The behavioral analytics filters can be strategically placed across customer touch points in the form of dynamic business rules. These filters can easily and inexpensively be created using behavioral analytic tools incorporating machine learning algorithms. These tools can be used to create dozens of decision tree which represent a series of predictive model.

As with Internet mechanisms, such as cookies, forms, beacons, and JavaScript – decision trees can be the building blocks to a strategic behavioral strategy and framework. Inductive decision tree software can be used to map from customer observations for targeting what kind of products or services to offer specific groups or classes of customers.

A valuable source of decision tree papers, tools and consultants can be found at KDnuggets.com (Knowledge Discovery) a premier site of analytical information and software. Decision trees are created using an assortment of algorithms and the cost can vary from a few thousand dollars to those that are free. Both decision tree tools and software capable of extracting IF/THEN rules can serve as filters to issue alerts and targeted communications to customers by enterprises.

These behavioral analytic filters or business rules describe the operations, definitions and constraints – as well as the opportunities and conditions which can be applied to enhance growth and revenue for enterprises. The behaviors of consumers provide the framework for applying these analytical tools to derive rules.

Two unique companies provide extremely cost effective behavioral analytical solutions. The first is RANK from Vadis.com which focuses on embedding modeling best practices instead of running behind multiple algorithms as most traditional analytical toolboxes do, such as SAS Enterprise Miner or SPPS Clementine. Vadis believes that the real challenge for building robust models, behavioral analytical filters, lies in the methodology the analyst uses, and in his business understanding of the behavior he is modeling. They offer their software for a free evaluation for several weeks.

The second company is even more intriguing and cost effective since ADAPA® (Adaptive Decision And Predictive Analytics) from Zementis.com offers behavioral analytics as a service with a pay as you go pricing model. ADAPA® is a decision engine framework for real-time execution of predictive models and rules. Models are deployed through an intuitive, Web-based management console, allowing the user to manage the deployment of decision models in a central repository.

This Service Oriented Architecture (SOA) results in an analytical service which simplifies integration with existing IT infrastructure for enterprises. To facilitate loosely-coupled integration with their in-house applications, ADAPA® implements the Java Data Mining (JDM) specification for its programming API, both at the Java and the Web Services level. ADAPA is based on the Amazon Elastic Computed Cloud (EC2) with virtually unlimited scalability; billing is handled via the Amazon Payment Service.

Enterprises can subscribe to the ADAPA analytical service to analyze and score data records against any of the models deployed in the ADAPA® engine from within their own environment by calling the ADAPA® web service. With the simple execution of a web service call, enterprises are able to leverage the power of predictive models and behavioral analytical filters almost instantaneously. This enables enterprises to employ best-practices for software architecture while protecting their long-term investment and legacy systems through open standards. Zementis includes statistical algorithms, machine learning, neural networks, and intelligent systems to extract hidden patterns from a variety of data types.

Imbedding these filters at websites, call sites and other operational systems can be used to build and manage customer relationships. Recognize that developing, testing and deploying these filters is a learning process conducted in an iterative fashion. The end result will be twofold: knowledge discovery and improved growth for enterprises. The insight gained will influence strategic direction as well as improved relevance in how enterprises communicate with each of their customers. The need for generating and evolving a set of filters based on relevant customer behavior patterns in enterprise data is fundamental to enabling personalized communications and targeted customer services across all channels. Effective behavioral analytical solutions require a set of inductive business rules that facilitate the automation of processes, the framework for accomplishing and leveraging these filters should be flexible and ongoing as conditions change. Radically different architecture of real-time streaming analytical software exists which automates the process of filter construction and maintenance and will be covered in the section on streaming analytic software.